Docker Image vs Container: Comparison, Properties, Collaboration, When to Use

Ever wondered whether you’re supposed to “fix the image” or “restart the container” when something breaks in Docker? This article walks through exactly that. We’ll clarify the difference between a Docker image and a Docker container, define each one in simple terms, and show how they work together in the Docker lifecycle. You’ll see how their properties differ, which commands manage them, when to use images vs containers, why they power modern DevOps workflows, and how they compare to virtual machines and orchestration tools like Kubernetes.

What is the difference between a Docker image and a container?

How do Docker images and containers work together in the Docker lifecycle?  

Docker images and containers work together in a very strict way in the Docker lifecycle: the image is the immutable blueprint that defines what a container should look like, and the container is the running, isolated instance created from that image. You always build and version images, then run and replace containers from those images as your application evolves.

The following points are related to how Docker images and containers interact across the Docker lifecycle.

• Image = immutable blueprint
• A Docker image is a read-only packaged filesystem plus metadata (built from a Dockerfile). It defines the base OS, dependencies, application code, and default command.
• Build phase

docker build takes a Dockerfile and produces an image by stacking layers for each instruction. Every instruction creates a new image layer, making builds efficient and reusable. The result is a versioned artifact like my-app:1.0, not a running process.

This layering begins with a base image, which provides the foundational filesystem layer that all subsequent layers build upon.

• Store and distribute

Images are pushed to a registry (Docker Hub or private) and pulled by other environments. This ensures the same image runs in dev, staging, and production.

• Container = running instance

docker run my-app:1.0 creates a container by combining the image’s read-only layers with a thin writable layer and starting the defined process in isolation.

• Multiple containers from one image

Many containers can run from the same image simultaneously, each with its own writable layer, configuration, and lifecycle, but sharing the same underlying image.

• Updates via new images, not long-lived containers

To update, you rebuild a new image version, push it, stop/remove old containers, and start new ones from the updated image. Containers are treated as disposable.

• End of lifecycle

When no longer needed, containers are stopped and removed, and unused images are eventually deleted from local storage or superseded in the registry.

Docker Image vs Container vs Kubernetes: What’s the Difference?

Docker images, containers, and Kubernetes sit at different layers of the container stack. The image is the packaged blueprint, the container is the running instance of that blueprint, and Kubernetes is the orchestration layer that manages many containers across machines.

The following points are related to the core differences between Docker images, Docker containers, and Kubernetes.

How do the properties of Docker images and containers differ?

When should you use Docker images vs Docker containers?

Use Docker images when you are defining, standardising, or distributing your application; use Docker containers when you actually need that application to run.

You should focus on Docker images when you:

• Define the application environment once and reuse it many times (base OS, runtime, dependencies, configuration).  
• Write or update the Dockerfile, since it defines the step-by-step instructions for building the container-ready filesystem and entrypoint that ultimately become part of the image.
• Need a versioned, immutable artifact to promote through CI/CD (build → test → staging → production) without changes.  
• Share or distribute software across teams, hosts, or clouds via a registry (the image is the portable unit).  
• Enforce compliance and security baselines, since images are easier to scan, sign, and control than live instances.  

You should focus on Docker containers when you:

• Need the application to actually execute on a host and, with an SBOM in place for transparency providing a detailed inventory of all components and dependencies, serve traffic, run jobs, or process data.  
• Configure runtime settings such as environment variables, ports, volumes, and resource limits specific to each environment.  
• Scale your workload horizontally by running many instances of the same image (for example, multiple web server replicas).  
• Observe and manage live behaviour: logs, metrics, health probes, restarts, and rolling updates.  
• Work with orchestrators like Kubernetes or Swarm, which schedule and manage containers across a cluster, including the container entrypoint that defines the default command or process that runs when the container starts, not the images themselves.

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Why are Docker images and containers used in modern development workflows?

Docker images and containers are used in modern development workflows because they make applications reproducible, portable, and easy to automate from laptop to production, especially when shared through a container registry, which serves as a centralized repository for storing, managing, and distributing container images.

• Consistent environments
  A Docker image bundles code, dependencies, and OS user space into one artifact, so the same build runs the same way on a laptop, CI server, and production cluster.  
• Reproducible, versioned releases
  The Dockerfile provides clear instructions for creating a container-ready image, so every change produces a new, traceable version you can promote or roll back.  
• Lightweight isolation and density
  Containers share the host kernel but isolate processes and filesystems, so teams can run many services on the same hardware with far less overhead than VMs.  
• Fast startup and scalable microservices
  Containers start in seconds, which makes auto-scaling APIs, short-lived CI jobs, and ephemeral test environments practical and cost-efficient.  
• Tight CI/CD and cloud integration
  Modern pipelines and platforms (including Kubernetes and managed container services) treat images as the standard delivery unit and containers as the standard runtime, making deployment automation straightforward.  

FAQs

1 Can I run a Docker container without a Docker image?

No. Every Docker container is an instance of a Docker image. The Docker Engine always needs an image (for example, my-api:1.2.3) as the immutable source to create and run a container process.

2 Can one Docker image be used to run multiple applications?

You can bundle multiple processes into one Docker image, but it is a bad practice. In modern containerization, one image should package one main application to keep scaling, logging, and failure isolation clean and predictable.

3 How do tags on Docker images affect containers?

A tag (for example, service-backend:2.0.0) identifies a specific version of an image. Containers created from that tag keep running even if you later push a new image to the same tag; you must recreate containers to pick up the new version.

4 How do Docker images and containers relate to security scanning?

Security tools usually scan Docker images at build or registry time, because the image is immutable and easier to analyze. Containers inherit those image vulnerabilities; fixing issues means rebuilding the image with updated packages and then recreating containers.

5 Does data written by a container change the underlying image?

No. The image stays read-only. Any data a container writes goes to its writable layer or attached volumes. When the container is removed, its writable layer is discarded and the original image remains unchanged.